The Science of Smarter Predictions

About

The field of predictive analysis in computer science is a rapidly developing area within Artificial Intelligence. Predictive analysis (predictive analytics) refers to the use of algorithms, data, and computational models to forecast future outcomes based on historical data.

This course introduces the fundamentals of predictive analysis through applications in sports analytics and prediction systems. Through a carefully curated series of lectures and workshops, you will learn the fundamentals of predictive analysis, develop your own prediction models for a range of sports, political events and weather phenomena and present your models to journals and to the Kuhiro Class community.

Mentors

Nwanacho Nwana is a graduate from the Massacutues Insttute of Technology, an independent researcher and author. He has previously received grant funding from MIT's Integrated Learning Initiative.

Dates

MAY 11

Mondays, Thursdays and Saturdays

6 weeks

7.30 PM NPT/ 7.45 PM IST

Eligibility

High School and above with demonstrated interest in probability and data analysis.

r/TheMatpatEffect

Registration

Prerequisites

Students are expected to have a foundational background in computer science, along with a working knowledge of Python and its core libraries (such as NumPy and pandas). A basic understanding of probability theory and statistics is essential, as the course builds on these concepts to develop and evaluate predictive models.

Who This Is For

This course is designed for students who are curious about:

Statistics, probability, and data science
Economics, forecasting, and prediction markets
How platforms like Kalshi, Polymarket, or ESPN Analytics operate
Building systems that go live and generate real-world signals—not just static reports
Conducting research that can be published, cited, or extended into future academic or professional work

Project Focus:

In the duration of the course, you are required to. Select any one market to create prediction models for:

Football, Basketball, Cricket, Tennis, Weather Patterns, Political Events among a range of pattern-based data sources to choose from.

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Nwanacho Nwana | Lead Mentor

Nwanacho Nwana is an educator, researcher, and writer who currently teaches mathematics at the District of Columbia International School (DCI). He is deeply passionate about leveraging data and AI to address educational challenges, particularly in public speaking anxiety and learning equity. As co-founder and Head of Product at Valfee, he has built an app-based platform that uses AI to help students and young entrepreneurs improve through mentorship, deliberate practice, and observational learning. He is a strong advocate for neurodivergent and underrepresented students and integrates his scholarly approach with social impact

Prabigya Acharya | Supporting Mentor

Prabigya Acharya is a Research Assistant at NAAMII, specializing in Computer Vision and AI research. He graduated in Computer Engineering from Pulchowk Campus, IOE. His interests include machine learning, deep learning, and building intelligent vision-based and agentic systems.

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Chirag Dahal | Supporting Mentor

Chirag Dahal is a graduate of Global School of Science with a strong interest in data science and analytical modelling. In the Basketball IQ project, he contributed to data collection, preprocessing, and statistical analysis, supporting the development and evaluation of predictive performance models. He aims to further pursue studies in computer science and data-driven research.

Throughout the course, you will develop a rigorous understanding of predictive analysis through a structured combination of lectures, applied assignments, and collaborative work with peers. The programme emphasizes not only theoretical foundations but also practical implementation, guiding you from data sourcing and model construction to validation, calibration, and real-world deployment

Class 1 | May 11 | Monday

What Is a Predictive Model? Markets, Edges, and the Modeller’s Mindset

Focus: Establish how prediction markets work, with emphasis on Kalshi’s alternative market categories. Introduce the concept of an edge: when your probability estimate differs from the market price. The central argument of this class: alternative markets (pop culture, weather, social media) are less efficient than sports because fewer people are modelling them seriously.

Case Study: Kalshi: “Will Taylor Swift announce a new album before June?” The market is at 38%. What data would you use to form a better estimate? Social media volume, label release patterns, tour scheduling, and press activity.

Class 2 | May 13 | Thursday

The Alternative Market Landscape: Where the Edge Lives

Focus: Survey the full landscape of non-sports prediction markets. Understand why pop culture, weather, social media, and economic markets are often mispriced: less modeller attention, noisier public sentiment, and data that requires more creative sourcing. Identify which market categories offer the best modelling opportunity.

Case Study: Comparing three Kalshi categories: Weather markets (strong historical data, well- understood physics, NWS forecasts available), pop culture markets (messy signals, social data, harder to model but thinner competition), economic markets (rich data, but consensus is strong and hard to beat). Where is the real edge?

Vibe Coding Session | May 15 | Saturday

Class 3 | May 18 | Monday

Sourcing Data for Unconventional Markets

Focus: Data for pop culture, weather, and social media markets doesn’t come from clean sports APIs. Students learn to find, access, and evaluate unconventional data sources: social APIs, government datasets, web scraping, search trends, and satellite data. Emphasis on creative data sourcing as a competitive advantage.

Case Study: Kalshi: “Will a specific song hit #1 on Billboard Hot 100?” Where does the data live? Spotify stream counts (via API), YouTube views, TikTok trend data (Chartmetric), radio airplay (Luminate), and prior chart trajectory. How do you turn these into a prediction?

Class 4 | May 20 | Thursday

The Alternative Market Landscape: Where the Edge Lives

Focus: Raw data from social APIs and unconventional sources often do not integrate directly into a model. Students learn to engineer features specific to alternative markets: momentum metrics from social data, anomaly detection in trend data, sentiment scoring, and how to handle the high noise-to-signal ratio in pop culture prediction.

Case Study: Kalshi: “Will [TV show] be renewed for another season?” Raw features: current viewership (Nielsen), social media mentions, IMDb rating, streaming platform rank. Engineered features: viewership trend (last 3 episodes vs. season avg), sentiment momentum (positive mention rate change week-over-week), platform rank stability. Which captures more signal?

Vibe Coding Session | May 22 | Saturday

Class 5| May 25 | Monday

Building and Backtesting Your First Alternative Market Model

Focus: Build a working model and backtest it correctly. Alternative markets often have smaller historical samples than sports — students learn to handle low-n environments, find analogous historical events, and set honest expectations about what a small backtest can and cannot prove.

Case Study: Kalshi: “Will it snow more than 1 inch in Chicago on Christmas?” — Historical NWS data goes back decades. Build a climatological baseline (historical frequency), then add forecast-based features (5-day NWS forecast, ensemble model agreement). How much does the forecast improve on the base rate?

Class 6| May 27 | Thursday

Calibration: Making Your Probabilities Honest

Focus: Calibration is especially important in alternative markets where the base rate may be extreme (very high or very low probability events). A model that says 85% should be right 85% of the time. Students learn to build calibration plots, compute Brier scores, and apply corrections when their model is systematically over- or underconfident.

Case Study: Kalshi: Political outcome markets — Models trained on polling data often systemat- ically underestimate upset probability. Walk through how to detect this in a calibration curve and apply isotonic regression to correct it — then compare Brier scores before and after.

Vibe Coding Session | May 29 | Saturday

Class 7 | June 1 | Monday

Tracking Live Predictions: Building Your Performance Record

Focus: Move from backtesting to live prediction tracking. Students log real Kalshi market predictions against their model output, track closing line value (CLV), and build a performance dashboard that will be part of their competition submission.

Case Study: IQ Project tracking methodology — Walk through how IQ tracks predictions across markets: the log structure, what metrics are checked weekly, and why closing line value (CLV) — how your prediction compares to the final market price before resolution — is the primary signal of model quality, not win rate alone

Class 8 | June 3 | Thursday

Systematic Improvement: Diagnosing and Fixing Model Weaknesses

No model is right on launch. The skill is diagnosing failure modes and improving without overfitting to noise. Students segment their performance, find where the model loses edge, and test one targeted improvement at a time.

Case Study: Kalshi: Social media milestone markets — A “Will [artist] reach 10M followers by [date]?” model underperforms for artists with recent viral events. Adding a “viral spike in last 30 days” binary feature to v2 and testing whether it fixes the gap without hurting baseline cases.

Vibe Coding Session| June 5 | Saturday

Class 9 | June 8 | Monday

Expanding Across Market Categories

Focus: Apply the framework to a second and third market type. The goal is speed and breadth — students have already learned the methodology, now they practice applying it to unfamiliar market categories quickly. This is where the competition is often won.

Case Study: From weather to pop culture — A student with a working Chicago snowfall model rapidly builds a “Will [film] gross over $50M opening weekend?” model. What transfers (regression structure, calibration, tracking setup), what changes (data sources, feature logic, base rate range), and what’s surprising.

Class 10 | June 10 | Thursday

The Alternative Market Landscape: Where the Edge Lives

Focus: Organize work into a defensible model portfolio. Judges will probe methodology, data sourcing decisions, and whether students can honestly characterise the limitations of their own models. The best presentations acknowledge weakness — that’s a sign of a real modeller.

Vibe Coding Session | June 12 | Saturday

In 2024, Kalshi became the first federally regulated prediction market in the United States, allowing people to trade contracts on real-world outcomes, elections, economic indicators, sports results, and more. Polymarket processed over $3 billion in volume during the 2024 election cycle. Platforms like these are not going away.

They are growing into a new layer of how the world prices uncertainty. And yet most of the participants in these markets from casual users to professional traders are working with gut feel, incomplete data, or models that have never been validated. The question this program asks is simple:

"Can a transparent, walk-forward validated statistical model beat the market consistently, and with documented proof?"

Real Research

This course build's on Kuhiro Class' Basketball IQ project which has already answered this question for college basketball: yes. Across 1,544 games, the Basketball IQ model outperformed Kalshi's predictions by 1.5 percentage points (70.1% vs 68.6%).

That result is time-stamped, walk-forward validated, and publicly verifiable. It has been presented at the MIT GenAI Conference and Columbia's Learning Ideas Conference.The gap we are now trying to close is coverage. One sport has been proven. Twelve more are waiting. That is the research mandate for this program.

Student Evaluation & Certification

Students will receive a letter of engagement upon completing the project, and mentors will evaluate their performance to issue an end-of-project recommendation letter where appropriate. Throughout the program, mentors will assess each student’s engagement during group sessions, the quality and timeliness of their assigned work, and their creativity in developing solutions to group tasks.

Patent and Copyrights

All intellectual property, copyrights, and potential patents arising from this project will be owned solely by the mentor. Distribution of any ownership rights, authorship credit, or derivative use permissions to participating group members will be at the mentor’s discretion and may be allocated based on class performance, contribution level, and the overall quality of engagement throughout the project.

Expert Mentors

Classes will be led by Natcho, who has years of experience working with students around the world, while Chirag and Prabigya, Kuhiro Class Alumini, will lead the vibe coding sessions.

Vibe Coding Sessions

Every Saturday, you meet your classmates to work together on strengthening your research and seniors to go over cocnepts you are confused about.

Publication Support

This program incentivises genuine research, and your mentors will guide you in ensuring your applications are submitted to the right places.

Kuhiro Class Credits

All candidates receive 30 credits to use across the Kuhiro Class platform, connecting with mentors from around the world for support on college applications or research papers.

Featuring

Register now

Sign up for this course by using the registration link below. Seats are limited to between 8-12 and guaged based on student's past perfomances.

Fee

USD 500

SAARC: 25,000 INR

Nepalese students: 25,000 NPR

Kuhiro Class

कुहिरो भित्र स्कुल

Prerequisites

Who This Is For

Project Focus:

Nwanacho Nwana | Lead Mentor

Prabigya Acharya | Supporting Mentor

Chirag Dahal | Supporting Mentor

Class 1 | May 11 | Monday

What Is a Predictive Model? Markets, Edges, and the Modeller’s Mindset

Class 3 | May 18 | Monday

Class 5| May 25 | Monday

Building and Backtesting Your First Alternative Market Model

Class 7 | June 1 | Monday

Tracking Live Predictions: Building Your Performance Record

Class 9 | June 8 | Monday

Expanding Across Market Categories

Final Competition | JUNE 19 | Judged Model Showcase

Register now

Sign up for this course by using the registration link below. Seats are limited to between 8-12 and guaged based on student's past perfomances.

Fee

Up to two scholarships of 100% are available based on a one-day exam post-registration. The course fee covers three days of classes per week.

Kuhiro Class

कुहिरो भित्र स्कुल

Class 1 | May 11 | Monday

​What Is a Predictive Model? Markets, Edges, and the Modeller’s Mindset

Register now

Sign up for this course by using the registration link below. Seats are limited to between 8-12 and guaged based on student's past perfomances.

Fee

Up to two scholarships of 100% are available based on a one-day exam post-registration. The course fee covers three days of classes per week.

What Is a Predictive Model? Markets, Edges, and the Modeller’s Mindset